{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TimeEval parameter optimization result analysis (Part 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import json\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pathlib import Path\n",
    "from timeeval import Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available result directories:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/results/2021-10-11_optim-part4'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_shared-optim'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-06_optim-part1'),\n",
       " PosixPath('/home/projects/akita/results/2021-09-27_default-params-1&2&3&4-merged'),\n",
       " PosixPath('/home/projects/akita/results/.ipynb_checkpoints'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-08_optim-part3'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-07_optim-part2'),\n",
       " PosixPath('/home/projects/akita/results/2021-10-12_optim-part5')]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data path: /home/projects/akita/data/test-cases\n",
      "Result path: /home/projects/akita/results/2021-10-08_optim-part3\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "data_path = Path(\"/home/projects/akita/data\") / \"test-cases\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\")\n",
    "experiment_result_folder = \"2021-10-08_optim-part3\"\n",
    "\n",
    "# build paths\n",
    "result_paths = [d for d in result_root_path.iterdir() if d.is_dir()]\n",
    "print(\"Available result directories:\")\n",
    "display(result_paths)\n",
    "\n",
    "result_path = result_root_path / experiment_result_folder\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data path: {data_path.resolve()}\")\n",
    "print(f\"Result path: {result_path.resolve()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results and dataset metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading results from /home/projects/akita/results/2021-10-08_optim-part3\n"
     ]
    }
   ],
   "source": [
    "# load results\n",
    "print(f\"Reading results from {result_path.resolve()}\")\n",
    "df = pd.read_csv(result_path / \"results.csv\")\n",
    "\n",
    "# add dataset_name column\n",
    "df[\"dataset_name\"] = df[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "# load dataset metadata\n",
    "dmgr = Datasets(data_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract target optimized parameter names that were iterated in this run (per algorithm):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MedianMethod []\n",
      "MultiHMM ['discretizer']\n",
      "Normalizing Flows ['teacher_epochs', 'distillation_iterations']\n",
      "NumentaHTM ['alpha']\n",
      "OceanWNN ['wavelet_wbf', 'wavelet_cs_C', 'wavelet_k', 'wavelet_a']\n"
     ]
    }
   ],
   "source": [
    "algo_param_mapping = {}\n",
    "algorithms = df[\"algorithm\"].unique()\n",
    "param_ignore_list = [\"max_anomaly_window_size\", \"anomaly_window_size\", \"neighbourhood_size\", \"window_size\", \"n_init_train\", \"embed_dim_range\"]\n",
    "\n",
    "for algo in algorithms:\n",
    "    param_sets = df.loc[df[\"algorithm\"] == algo, \"hyper_params\"].unique()\n",
    "    param_sets = [json.loads(ps) for ps in param_sets]\n",
    "    param_names = np.unique([name for ps in param_sets for name in ps if name not in param_ignore_list])\n",
    "    search_space = set()\n",
    "    for param_name in param_names:\n",
    "        values = []\n",
    "        for ps in param_sets:\n",
    "            try:\n",
    "                values.append(ps[param_name])\n",
    "            except:\n",
    "                pass\n",
    "        values = np.unique(values)\n",
    "        if values.shape[0] > 1:\n",
    "            search_space.add(param_name)\n",
    "    algo_param_mapping[algo] = list(search_space)\n",
    "\n",
    "for algo in algo_param_mapping:\n",
    "    print(algo, algo_param_mapping[algo])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract optimized parameters and their values (columns: optim_param_name and optim_param_value) for each experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_hyper_params(algo):\n",
    "    param_names = algo_param_mapping[algo]\n",
    "    def extract(value):\n",
    "        params = json.loads(value)\n",
    "        result = None\n",
    "        for name in param_names:\n",
    "            try:\n",
    "                value = params[name]\n",
    "                result = pd.Series([name, value], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "                break\n",
    "            except KeyError:\n",
    "                pass\n",
    "        if result is None:\n",
    "            return pd.Series([np.nan, np.nan], index=[\"optim_param_name\", \"optim_param_value\"])\n",
    "        return result\n",
    "    return extract\n",
    "\n",
    "df[[\"optim_param_name\", \"optim_param_value\"]] = \"\"\n",
    "for algo in algo_param_mapping:\n",
    "    df_algo = df.loc[df[\"algorithm\"] == algo]\n",
    "    df.loc[df_algo.index, [\"optim_param_name\", \"optim_param_value\"]] = df_algo[\"hyper_params\"].apply(extract_hyper_params(algo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract window size parameters (dependent params) and convert them into multiples of the dataset period size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dependent_param_names = [\"neighbourhood_size\", \"window_size\"]\n",
    "\n",
    "def extract_window_param(value, param_name=\"\"):\n",
    "    params = json.loads(value)\n",
    "    try:\n",
    "        return params[param_name]\n",
    "    except KeyError:\n",
    "        return 0\n",
    "\n",
    "for param_name in dependent_param_names:\n",
    "    s_windows = df[\"hyper_params\"].apply(extract_window_param, param_name=param_name)\n",
    "    df2 = df[s_windows > 0][[\"dataset\"]].copy()\n",
    "    df2[param_name] = s_windows[df2.index]\n",
    "    df2[\"period_size\"] = df2[\"dataset\"].apply(lambda d: dmgr.get((\"GutenTAG\", d)).period_size)\n",
    "    df2[\"optim_param_name\"] = param_name\n",
    "    df2[\"optim_param_value\"] = df2[param_name] / df2[\"period_size\"]\n",
    "    df2[\"optim_param_value\"] = (df2[\"optim_param_value\"]\n",
    "                                .fillna(df2[param_name])\n",
    "                                .round(1)\n",
    "                                .replace(50., 0.5)\n",
    "                                .replace(100, 1.0)\n",
    "                                .replace(150, 1.5)\n",
    "                                .replace(200, 2.0))\n",
    "    df.loc[df2.index, [\"optim_param_name\", \"optim_param_value\"]] = df2[[\"optim_param_name\", \"optim_param_value\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, optim_params, repetition=1):\n",
    "    params_id = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1]) & (df[\"optim_param_name\"] == optim_params[0]) & (df[\"optim_param_value\"] == optim_params[1]), \"hyper_params_id\"].item()\n",
    "    path = (\n",
    "        result_path /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        dataset_id[1] /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define plotting functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_use_plotly = True\n",
    "try:\n",
    "    import plotly.offline\n",
    "except ImportError:\n",
    "    default_use_plotly = False\n",
    "\n",
    "def plot_scores(algorithm_name, dataset_name, use_plotly: bool = default_use_plotly, **kwargs):\n",
    "    if isinstance(algorithm_name, tuple):\n",
    "        algorithms = [algorithm_name]\n",
    "    elif not isinstance(algorithm_name, list):\n",
    "        raise ValueError(\"Please supply a tuple (algorithm_name, optim_param_name, optim_param_value) or a list thereof as first argument!\")\n",
    "    else:\n",
    "        algorithms = algorithm_name\n",
    "    # construct dataset ID\n",
    "    dataset_id = (\"GutenTAG\", f\"{dataset_name}.unsupervised\")\n",
    "\n",
    "    # load dataset details\n",
    "    df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "\n",
    "    # check if dataset is multivariate\n",
    "    dataset_dim = df.loc[df[\"dataset_name\"] == dataset_name, \"dataset_input_dimensionality\"].unique().item()\n",
    "    dataset_dim = dataset_dim.lower()\n",
    "    \n",
    "    auroc = {}\n",
    "    df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "    skip_algos = []\n",
    "    algos = []\n",
    "    for algo, optim_param_name, optim_param_value in algorithms:\n",
    "        optim_params = f\"{optim_param_name}={optim_param_value}\"\n",
    "        algos.append((algo, optim_params))\n",
    "        # get algorithm metric results\n",
    "        try:\n",
    "            auroc[(algo, optim_params)] = df.loc[\n",
    "                (df[\"algorithm\"] == algo) & (df[\"dataset_name\"] == dataset_name) & (df[\"optim_param_name\"] == optim_param_name) & (df[\"optim_param_value\"] == optim_param_value),\n",
    "                \"ROC_AUC\"\n",
    "            ].item()\n",
    "        except ValueError:\n",
    "            warnings.warn(f\"No ROC_AUC score found! Probably {algo} with params {optim_params} was not executed on {dataset_name}.\")\n",
    "            auroc[(algo, optim_params)] = -1\n",
    "            skip_algos.append((algo, optim_params))\n",
    "            continue\n",
    "\n",
    "        # load scores\n",
    "        training_type = df.loc[df[\"algorithm\"] == algo, \"algo_training_type\"].values[0].lower().replace(\"_\", \"-\")\n",
    "        try:\n",
    "            df_scores[(algo, optim_params)] = load_scores_df(algo, (\"GutenTAG\", f\"{dataset_name}.{training_type}\"), (optim_param_name, optim_param_value)).iloc[:, 0]\n",
    "        except (ValueError, FileNotFoundError):\n",
    "            warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name} with params {optim_params}.\")\n",
    "            df_scores[(algo, optim_params)] = np.nan\n",
    "            skip_algos.append((algo, optim_params))\n",
    "    algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "    if use_plotly:\n",
    "        return plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "    else:\n",
    "        return plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs)\n",
    "\n",
    "def plot_scores_plotly(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import plotly.offline as py\n",
    "    import plotly.graph_objects as go\n",
    "    import plotly.figure_factory as ff\n",
    "    import plotly.express as px\n",
    "    from plotly.subplots import make_subplots\n",
    "\n",
    "    # Create plot\n",
    "    fig = make_subplots(2, 1)\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, i], name=f\"channel-{i}\"), 1, 1)\n",
    "    else:\n",
    "        fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset.iloc[:, 1], name=\"timeseries\"), 1, 1)\n",
    "    fig.add_trace(go.Scatter(x=df_dataset.index, y=df_dataset[\"is_anomaly\"], name=\"label\"), 2, 1)\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        fig.add_trace(go.Scatter(x=df_scores.index, y=df_scores[item], name=f\"{algo}={auroc[item]:.4f} ({optim_params})\"), 2, 1)\n",
    "    fig.update_xaxes(matches=\"x\")\n",
    "    fig.update_layout(\n",
    "        title=f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\",\n",
    "        height=400\n",
    "    )\n",
    "    return py.iplot(fig)\n",
    "\n",
    "def plot_scores_plt(algorithms, auroc, df_scores, df_dataset, dataset_dim, dataset_name, **kwargs):\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    # Create plot\n",
    "    fig, axs = plt.subplots(2, 1, sharex=True, figsize=(20, 8))\n",
    "    if dataset_dim == \"multivariate\":\n",
    "        for i in range(1, df_dataset.shape[1]-1):\n",
    "            axs[0].plot(df_dataset.index, df_dataset.iloc[:, i], label=f\"channel-{i}\")\n",
    "    else:\n",
    "        axs[0].plot(df_dataset.index, df_dataset.iloc[:, 1], label=f\"timeseries\")\n",
    "    axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "    \n",
    "    for item in algorithms:\n",
    "        algo, optim_params = item\n",
    "        axs[1].plot(df_scores.index, df_scores[item], label=f\"{algo}={auroc[item]:.4f} ({optim_params})\")\n",
    "    axs[0].legend()\n",
    "    axs[1].legend()\n",
    "    fig.suptitle(f\"Results of {','.join(np.unique([a for a, _ in algorithms]))} on {dataset_name}\")\n",
    "    fig.tight_layout()\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameter assessment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">PR_AUC</th>\n",
       "      <th colspan=\"3\" halign=\"left\">ROC_AUC</th>\n",
       "      <th>train_main_time</th>\n",
       "      <th>execute_main_time</th>\n",
       "      <th>repetition</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>algorithm</th>\n",
       "      <th>optim_param_name</th>\n",
       "      <th>optim_param_value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">OceanWNN</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">wavelet_wbf</th>\n",
       "      <th>mexican_hat</th>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.325102</td>\n",
       "      <td>0.090919</td>\n",
       "      <td>0.137320</td>\n",
       "      <td>0.719094</td>\n",
       "      <td>0.732439</td>\n",
       "      <td>220.887725</td>\n",
       "      <td>9.788483</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>morlet</th>\n",
       "      <td>0.000086</td>\n",
       "      <td>0.331367</td>\n",
       "      <td>0.117366</td>\n",
       "      <td>0.106312</td>\n",
       "      <td>0.716719</td>\n",
       "      <td>0.733015</td>\n",
       "      <td>222.037403</td>\n",
       "      <td>9.593179</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>central_symmetric</th>\n",
       "      <td>0.000572</td>\n",
       "      <td>0.334198</td>\n",
       "      <td>0.110439</td>\n",
       "      <td>0.148325</td>\n",
       "      <td>0.706422</td>\n",
       "      <td>0.741509</td>\n",
       "      <td>247.322546</td>\n",
       "      <td>9.549337</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">wavelet_k</th>\n",
       "      <th>-1.9500000000000002</th>\n",
       "      <td>0.000080</td>\n",
       "      <td>0.333860</td>\n",
       "      <td>0.122270</td>\n",
       "      <td>0.147000</td>\n",
       "      <td>0.727068</td>\n",
       "      <td>0.756351</td>\n",
       "      <td>215.404850</td>\n",
       "      <td>9.914775</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.5</th>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.325038</td>\n",
       "      <td>0.088348</td>\n",
       "      <td>0.137320</td>\n",
       "      <td>0.714845</td>\n",
       "      <td>0.729408</td>\n",
       "      <td>239.119688</td>\n",
       "      <td>9.627418</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.0499999999999998</th>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.320639</td>\n",
       "      <td>0.099033</td>\n",
       "      <td>0.143298</td>\n",
       "      <td>0.711269</td>\n",
       "      <td>0.720220</td>\n",
       "      <td>244.418939</td>\n",
       "      <td>9.772062</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">wavelet_cs_C</th>\n",
       "      <th>2.275</th>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.323063</td>\n",
       "      <td>0.090919</td>\n",
       "      <td>0.137320</td>\n",
       "      <td>0.718599</td>\n",
       "      <td>0.727584</td>\n",
       "      <td>251.294496</td>\n",
       "      <td>9.637073</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.2249999999999999</th>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.322278</td>\n",
       "      <td>0.090025</td>\n",
       "      <td>0.137320</td>\n",
       "      <td>0.718337</td>\n",
       "      <td>0.732439</td>\n",
       "      <td>242.087609</td>\n",
       "      <td>9.740249</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.75</th>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.322359</td>\n",
       "      <td>0.090025</td>\n",
       "      <td>0.137320</td>\n",
       "      <td>0.716037</td>\n",
       "      <td>0.727584</td>\n",
       "      <td>246.578676</td>\n",
       "      <td>9.637457</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">wavelet_a</th>\n",
       "      <th>-3.25</th>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.326135</td>\n",
       "      <td>0.122953</td>\n",
       "      <td>0.142196</td>\n",
       "      <td>0.722390</td>\n",
       "      <td>0.783861</td>\n",
       "      <td>267.331575</td>\n",
       "      <td>9.618413</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-2.5</th>\n",
       "      <td>0.000469</td>\n",
       "      <td>0.322954</td>\n",
       "      <td>0.091337</td>\n",
       "      <td>0.137320</td>\n",
       "      <td>0.716189</td>\n",
       "      <td>0.718505</td>\n",
       "      <td>250.984491</td>\n",
       "      <td>9.746015</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-1.75</th>\n",
       "      <td>0.000673</td>\n",
       "      <td>0.333336</td>\n",
       "      <td>0.111123</td>\n",
       "      <td>0.058463</td>\n",
       "      <td>0.711607</td>\n",
       "      <td>0.707782</td>\n",
       "      <td>227.851405</td>\n",
       "      <td>9.709994</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">NumentaHTM</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">alpha</th>\n",
       "      <th>0.1</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.198526</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.249644</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.9</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.097278</td>\n",
       "      <td>0.036126</td>\n",
       "      <td>0.489009</td>\n",
       "      <td>0.564347</td>\n",
       "      <td>0.503068</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.266986</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Normalizing Flows</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">window_size</th>\n",
       "      <th>1.5</th>\n",
       "      <td>0.000539</td>\n",
       "      <td>0.736810</td>\n",
       "      <td>0.946671</td>\n",
       "      <td>0.004677</td>\n",
       "      <td>0.885549</td>\n",
       "      <td>0.998814</td>\n",
       "      <td>2095.199242</td>\n",
       "      <td>19.526353</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.000101</td>\n",
       "      <td>0.725940</td>\n",
       "      <td>0.882377</td>\n",
       "      <td>0.113787</td>\n",
       "      <td>0.884895</td>\n",
       "      <td>0.992403</td>\n",
       "      <td>2462.720821</td>\n",
       "      <td>15.234477</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.705029</td>\n",
       "      <td>0.955895</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.875150</td>\n",
       "      <td>0.997208</td>\n",
       "      <td>5217.494637</td>\n",
       "      <td>18.587911</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.000555</td>\n",
       "      <td>0.631446</td>\n",
       "      <td>0.789351</td>\n",
       "      <td>0.099840</td>\n",
       "      <td>0.840778</td>\n",
       "      <td>0.939847</td>\n",
       "      <td>2308.720736</td>\n",
       "      <td>17.071832</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">teacher_epochs</th>\n",
       "      <th>100.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.495994</td>\n",
       "      <td>0.496279</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750952</td>\n",
       "      <td>0.892011</td>\n",
       "      <td>1643.969280</td>\n",
       "      <td>14.560660</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.492523</td>\n",
       "      <td>0.482070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.749513</td>\n",
       "      <td>0.887612</td>\n",
       "      <td>1145.450923</td>\n",
       "      <td>14.332372</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.500970</td>\n",
       "      <td>0.515397</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.748558</td>\n",
       "      <td>0.881693</td>\n",
       "      <td>1425.029099</td>\n",
       "      <td>14.556057</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.490428</td>\n",
       "      <td>0.483341</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.748030</td>\n",
       "      <td>0.885083</td>\n",
       "      <td>1159.381175</td>\n",
       "      <td>14.692445</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">distillation_iterations</th>\n",
       "      <th>100.0</th>\n",
       "      <td>0.000099</td>\n",
       "      <td>0.463409</td>\n",
       "      <td>0.420193</td>\n",
       "      <td>0.004495</td>\n",
       "      <td>0.749691</td>\n",
       "      <td>0.881657</td>\n",
       "      <td>1239.862398</td>\n",
       "      <td>14.610158</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.508525</td>\n",
       "      <td>0.540947</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.749336</td>\n",
       "      <td>0.891165</td>\n",
       "      <td>1303.499523</td>\n",
       "      <td>14.621725</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.499098</td>\n",
       "      <td>0.510331</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.744304</td>\n",
       "      <td>0.882432</td>\n",
       "      <td>1278.521155</td>\n",
       "      <td>14.762646</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.0</th>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.499257</td>\n",
       "      <td>0.523585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.744149</td>\n",
       "      <td>0.885128</td>\n",
       "      <td>1289.645498</td>\n",
       "      <td>14.852075</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">MultiHMM</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">discretizer</th>\n",
       "      <th>choquet</th>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.081607</td>\n",
       "      <td>0.021657</td>\n",
       "      <td>0.047605</td>\n",
       "      <td>0.481271</td>\n",
       "      <td>0.490191</td>\n",
       "      <td>55.586170</td>\n",
       "      <td>6.355054</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fcm</th>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.075718</td>\n",
       "      <td>0.013234</td>\n",
       "      <td>0.047605</td>\n",
       "      <td>0.479806</td>\n",
       "      <td>0.486645</td>\n",
       "      <td>124.418148</td>\n",
       "      <td>6.795746</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sugeno</th>\n",
       "      <td>0.000052</td>\n",
       "      <td>0.074048</td>\n",
       "      <td>0.015622</td>\n",
       "      <td>0.047605</td>\n",
       "      <td>0.479646</td>\n",
       "      <td>0.486645</td>\n",
       "      <td>56.252017</td>\n",
       "      <td>6.490469</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MedianMethod</th>\n",
       "      <th rowspan=\"4\" valign=\"top\">neighbourhood_size</th>\n",
       "      <th>1.0</th>\n",
       "      <td>0.001464</td>\n",
       "      <td>0.198292</td>\n",
       "      <td>0.070405</td>\n",
       "      <td>0.002020</td>\n",
       "      <td>0.646185</td>\n",
       "      <td>0.598712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.542016</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.0</th>\n",
       "      <td>0.000794</td>\n",
       "      <td>0.213335</td>\n",
       "      <td>0.074287</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>0.641156</td>\n",
       "      <td>0.561343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.195646</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.5</th>\n",
       "      <td>0.000913</td>\n",
       "      <td>0.205680</td>\n",
       "      <td>0.076479</td>\n",
       "      <td>0.003030</td>\n",
       "      <td>0.637018</td>\n",
       "      <td>0.565943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.488536</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.002220</td>\n",
       "      <td>0.154124</td>\n",
       "      <td>0.041438</td>\n",
       "      <td>0.001010</td>\n",
       "      <td>0.587037</td>\n",
       "      <td>0.562033</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.533590</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 PR_AUC  \\\n",
       "                                                                    min   \n",
       "algorithm         optim_param_name        optim_param_value               \n",
       "OceanWNN          wavelet_wbf             mexican_hat          0.000469   \n",
       "                                          morlet               0.000086   \n",
       "                                          central_symmetric    0.000572   \n",
       "                  wavelet_k               -1.9500000000000002  0.000080   \n",
       "                                          -1.5                 0.000469   \n",
       "                                          -1.0499999999999998  0.000657   \n",
       "                  wavelet_cs_C            2.275                0.000469   \n",
       "                                          1.2249999999999999   0.000469   \n",
       "                                          1.75                 0.000469   \n",
       "                  wavelet_a               -3.25                0.000073   \n",
       "                                          -2.5                 0.000469   \n",
       "                                          -1.75                0.000673   \n",
       "NumentaHTM        alpha                   0.1                  0.000500   \n",
       "                                          0.5                  0.000500   \n",
       "                                          0.9                  0.000500   \n",
       "Normalizing Flows window_size             1.5                  0.000539   \n",
       "                                          1.0                  0.000101   \n",
       "                                          2.0                  0.000500   \n",
       "                                          0.5                  0.000555   \n",
       "                  teacher_epochs          100.0                0.000050   \n",
       "                                          20.0                 0.000050   \n",
       "                                          50.0                 0.000050   \n",
       "                                          10.0                 0.000050   \n",
       "                  distillation_iterations 100.0                0.000099   \n",
       "                                          10.0                 0.000050   \n",
       "                                          50.0                 0.000050   \n",
       "                                          20.0                 0.000050   \n",
       "MultiHMM          discretizer             choquet              0.000052   \n",
       "                                          fcm                  0.000052   \n",
       "                                          sugeno               0.000052   \n",
       "MedianMethod      neighbourhood_size      1.0                  0.001464   \n",
       "                                          2.0                  0.000794   \n",
       "                                          1.5                  0.000913   \n",
       "                                          0.5                  0.002220   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                   mean   \n",
       "algorithm         optim_param_name        optim_param_value               \n",
       "OceanWNN          wavelet_wbf             mexican_hat          0.325102   \n",
       "                                          morlet               0.331367   \n",
       "                                          central_symmetric    0.334198   \n",
       "                  wavelet_k               -1.9500000000000002  0.333860   \n",
       "                                          -1.5                 0.325038   \n",
       "                                          -1.0499999999999998  0.320639   \n",
       "                  wavelet_cs_C            2.275                0.323063   \n",
       "                                          1.2249999999999999   0.322278   \n",
       "                                          1.75                 0.322359   \n",
       "                  wavelet_a               -3.25                0.326135   \n",
       "                                          -2.5                 0.322954   \n",
       "                                          -1.75                0.333336   \n",
       "NumentaHTM        alpha                   0.1                  0.097278   \n",
       "                                          0.5                  0.097278   \n",
       "                                          0.9                  0.097278   \n",
       "Normalizing Flows window_size             1.5                  0.736810   \n",
       "                                          1.0                  0.725940   \n",
       "                                          2.0                  0.705029   \n",
       "                                          0.5                  0.631446   \n",
       "                  teacher_epochs          100.0                0.495994   \n",
       "                                          20.0                 0.492523   \n",
       "                                          50.0                 0.500970   \n",
       "                                          10.0                 0.490428   \n",
       "                  distillation_iterations 100.0                0.463409   \n",
       "                                          10.0                 0.508525   \n",
       "                                          50.0                 0.499098   \n",
       "                                          20.0                 0.499257   \n",
       "MultiHMM          discretizer             choquet              0.081607   \n",
       "                                          fcm                  0.075718   \n",
       "                                          sugeno               0.074048   \n",
       "MedianMethod      neighbourhood_size      1.0                  0.198292   \n",
       "                                          2.0                  0.213335   \n",
       "                                          1.5                  0.205680   \n",
       "                                          0.5                  0.154124   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                 median   \n",
       "algorithm         optim_param_name        optim_param_value               \n",
       "OceanWNN          wavelet_wbf             mexican_hat          0.090919   \n",
       "                                          morlet               0.117366   \n",
       "                                          central_symmetric    0.110439   \n",
       "                  wavelet_k               -1.9500000000000002  0.122270   \n",
       "                                          -1.5                 0.088348   \n",
       "                                          -1.0499999999999998  0.099033   \n",
       "                  wavelet_cs_C            2.275                0.090919   \n",
       "                                          1.2249999999999999   0.090025   \n",
       "                                          1.75                 0.090025   \n",
       "                  wavelet_a               -3.25                0.122953   \n",
       "                                          -2.5                 0.091337   \n",
       "                                          -1.75                0.111123   \n",
       "NumentaHTM        alpha                   0.1                  0.036126   \n",
       "                                          0.5                  0.036126   \n",
       "                                          0.9                  0.036126   \n",
       "Normalizing Flows window_size             1.5                  0.946671   \n",
       "                                          1.0                  0.882377   \n",
       "                                          2.0                  0.955895   \n",
       "                                          0.5                  0.789351   \n",
       "                  teacher_epochs          100.0                0.496279   \n",
       "                                          20.0                 0.482070   \n",
       "                                          50.0                 0.515397   \n",
       "                                          10.0                 0.483341   \n",
       "                  distillation_iterations 100.0                0.420193   \n",
       "                                          10.0                 0.540947   \n",
       "                                          50.0                 0.510331   \n",
       "                                          20.0                 0.523585   \n",
       "MultiHMM          discretizer             choquet              0.021657   \n",
       "                                          fcm                  0.013234   \n",
       "                                          sugeno               0.015622   \n",
       "MedianMethod      neighbourhood_size      1.0                  0.070405   \n",
       "                                          2.0                  0.074287   \n",
       "                                          1.5                  0.076479   \n",
       "                                          0.5                  0.041438   \n",
       "\n",
       "                                                                ROC_AUC  \\\n",
       "                                                                    min   \n",
       "algorithm         optim_param_name        optim_param_value               \n",
       "OceanWNN          wavelet_wbf             mexican_hat          0.137320   \n",
       "                                          morlet               0.106312   \n",
       "                                          central_symmetric    0.148325   \n",
       "                  wavelet_k               -1.9500000000000002  0.147000   \n",
       "                                          -1.5                 0.137320   \n",
       "                                          -1.0499999999999998  0.143298   \n",
       "                  wavelet_cs_C            2.275                0.137320   \n",
       "                                          1.2249999999999999   0.137320   \n",
       "                                          1.75                 0.137320   \n",
       "                  wavelet_a               -3.25                0.142196   \n",
       "                                          -2.5                 0.137320   \n",
       "                                          -1.75                0.058463   \n",
       "NumentaHTM        alpha                   0.1                  0.489009   \n",
       "                                          0.5                  0.489009   \n",
       "                                          0.9                  0.489009   \n",
       "Normalizing Flows window_size             1.5                  0.004677   \n",
       "                                          1.0                  0.113787   \n",
       "                                          2.0                  0.000420   \n",
       "                                          0.5                  0.099840   \n",
       "                  teacher_epochs          100.0                0.000000   \n",
       "                                          20.0                 0.000000   \n",
       "                                          50.0                 0.000000   \n",
       "                                          10.0                 0.000000   \n",
       "                  distillation_iterations 100.0                0.004495   \n",
       "                                          10.0                 0.000000   \n",
       "                                          50.0                 0.000000   \n",
       "                                          20.0                 0.000000   \n",
       "MultiHMM          discretizer             choquet              0.047605   \n",
       "                                          fcm                  0.047605   \n",
       "                                          sugeno               0.047605   \n",
       "MedianMethod      neighbourhood_size      1.0                  0.002020   \n",
       "                                          2.0                  0.004040   \n",
       "                                          1.5                  0.003030   \n",
       "                                          0.5                  0.001010   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                   mean   \n",
       "algorithm         optim_param_name        optim_param_value               \n",
       "OceanWNN          wavelet_wbf             mexican_hat          0.719094   \n",
       "                                          morlet               0.716719   \n",
       "                                          central_symmetric    0.706422   \n",
       "                  wavelet_k               -1.9500000000000002  0.727068   \n",
       "                                          -1.5                 0.714845   \n",
       "                                          -1.0499999999999998  0.711269   \n",
       "                  wavelet_cs_C            2.275                0.718599   \n",
       "                                          1.2249999999999999   0.718337   \n",
       "                                          1.75                 0.716037   \n",
       "                  wavelet_a               -3.25                0.722390   \n",
       "                                          -2.5                 0.716189   \n",
       "                                          -1.75                0.711607   \n",
       "NumentaHTM        alpha                   0.1                  0.564347   \n",
       "                                          0.5                  0.564347   \n",
       "                                          0.9                  0.564347   \n",
       "Normalizing Flows window_size             1.5                  0.885549   \n",
       "                                          1.0                  0.884895   \n",
       "                                          2.0                  0.875150   \n",
       "                                          0.5                  0.840778   \n",
       "                  teacher_epochs          100.0                0.750952   \n",
       "                                          20.0                 0.749513   \n",
       "                                          50.0                 0.748558   \n",
       "                                          10.0                 0.748030   \n",
       "                  distillation_iterations 100.0                0.749691   \n",
       "                                          10.0                 0.749336   \n",
       "                                          50.0                 0.744304   \n",
       "                                          20.0                 0.744149   \n",
       "MultiHMM          discretizer             choquet              0.481271   \n",
       "                                          fcm                  0.479806   \n",
       "                                          sugeno               0.479646   \n",
       "MedianMethod      neighbourhood_size      1.0                  0.646185   \n",
       "                                          2.0                  0.641156   \n",
       "                                          1.5                  0.637018   \n",
       "                                          0.5                  0.587037   \n",
       "\n",
       "                                                                         \\\n",
       "                                                                 median   \n",
       "algorithm         optim_param_name        optim_param_value               \n",
       "OceanWNN          wavelet_wbf             mexican_hat          0.732439   \n",
       "                                          morlet               0.733015   \n",
       "                                          central_symmetric    0.741509   \n",
       "                  wavelet_k               -1.9500000000000002  0.756351   \n",
       "                                          -1.5                 0.729408   \n",
       "                                          -1.0499999999999998  0.720220   \n",
       "                  wavelet_cs_C            2.275                0.727584   \n",
       "                                          1.2249999999999999   0.732439   \n",
       "                                          1.75                 0.727584   \n",
       "                  wavelet_a               -3.25                0.783861   \n",
       "                                          -2.5                 0.718505   \n",
       "                                          -1.75                0.707782   \n",
       "NumentaHTM        alpha                   0.1                  0.503068   \n",
       "                                          0.5                  0.503068   \n",
       "                                          0.9                  0.503068   \n",
       "Normalizing Flows window_size             1.5                  0.998814   \n",
       "                                          1.0                  0.992403   \n",
       "                                          2.0                  0.997208   \n",
       "                                          0.5                  0.939847   \n",
       "                  teacher_epochs          100.0                0.892011   \n",
       "                                          20.0                 0.887612   \n",
       "                                          50.0                 0.881693   \n",
       "                                          10.0                 0.885083   \n",
       "                  distillation_iterations 100.0                0.881657   \n",
       "                                          10.0                 0.891165   \n",
       "                                          50.0                 0.882432   \n",
       "                                          20.0                 0.885128   \n",
       "MultiHMM          discretizer             choquet              0.490191   \n",
       "                                          fcm                  0.486645   \n",
       "                                          sugeno               0.486645   \n",
       "MedianMethod      neighbourhood_size      1.0                  0.598712   \n",
       "                                          2.0                  0.561343   \n",
       "                                          1.5                  0.565943   \n",
       "                                          0.5                  0.562033   \n",
       "\n",
       "                                                              train_main_time  \\\n",
       "                                                                         mean   \n",
       "algorithm         optim_param_name        optim_param_value                     \n",
       "OceanWNN          wavelet_wbf             mexican_hat              220.887725   \n",
       "                                          morlet                   222.037403   \n",
       "                                          central_symmetric        247.322546   \n",
       "                  wavelet_k               -1.9500000000000002      215.404850   \n",
       "                                          -1.5                     239.119688   \n",
       "                                          -1.0499999999999998      244.418939   \n",
       "                  wavelet_cs_C            2.275                    251.294496   \n",
       "                                          1.2249999999999999       242.087609   \n",
       "                                          1.75                     246.578676   \n",
       "                  wavelet_a               -3.25                    267.331575   \n",
       "                                          -2.5                     250.984491   \n",
       "                                          -1.75                    227.851405   \n",
       "NumentaHTM        alpha                   0.1                             NaN   \n",
       "                                          0.5                             NaN   \n",
       "                                          0.9                             NaN   \n",
       "Normalizing Flows window_size             1.5                     2095.199242   \n",
       "                                          1.0                     2462.720821   \n",
       "                                          2.0                     5217.494637   \n",
       "                                          0.5                     2308.720736   \n",
       "                  teacher_epochs          100.0                   1643.969280   \n",
       "                                          20.0                    1145.450923   \n",
       "                                          50.0                    1425.029099   \n",
       "                                          10.0                    1159.381175   \n",
       "                  distillation_iterations 100.0                   1239.862398   \n",
       "                                          10.0                    1303.499523   \n",
       "                                          50.0                    1278.521155   \n",
       "                                          20.0                    1289.645498   \n",
       "MultiHMM          discretizer             choquet                   55.586170   \n",
       "                                          fcm                      124.418148   \n",
       "                                          sugeno                    56.252017   \n",
       "MedianMethod      neighbourhood_size      1.0                             NaN   \n",
       "                                          2.0                             NaN   \n",
       "                                          1.5                             NaN   \n",
       "                                          0.5                             NaN   \n",
       "\n",
       "                                                              execute_main_time  \\\n",
       "                                                                           mean   \n",
       "algorithm         optim_param_name        optim_param_value                       \n",
       "OceanWNN          wavelet_wbf             mexican_hat                  9.788483   \n",
       "                                          morlet                       9.593179   \n",
       "                                          central_symmetric            9.549337   \n",
       "                  wavelet_k               -1.9500000000000002          9.914775   \n",
       "                                          -1.5                         9.627418   \n",
       "                                          -1.0499999999999998          9.772062   \n",
       "                  wavelet_cs_C            2.275                        9.637073   \n",
       "                                          1.2249999999999999           9.740249   \n",
       "                                          1.75                         9.637457   \n",
       "                  wavelet_a               -3.25                        9.618413   \n",
       "                                          -2.5                         9.746015   \n",
       "                                          -1.75                        9.709994   \n",
       "NumentaHTM        alpha                   0.1                         82.198526   \n",
       "                                          0.5                         82.249644   \n",
       "                                          0.9                         82.266986   \n",
       "Normalizing Flows window_size             1.5                         19.526353   \n",
       "                                          1.0                         15.234477   \n",
       "                                          2.0                         18.587911   \n",
       "                                          0.5                         17.071832   \n",
       "                  teacher_epochs          100.0                       14.560660   \n",
       "                                          20.0                        14.332372   \n",
       "                                          50.0                        14.556057   \n",
       "                                          10.0                        14.692445   \n",
       "                  distillation_iterations 100.0                       14.610158   \n",
       "                                          10.0                        14.621725   \n",
       "                                          50.0                        14.762646   \n",
       "                                          20.0                        14.852075   \n",
       "MultiHMM          discretizer             choquet                      6.355054   \n",
       "                                          fcm                          6.795746   \n",
       "                                          sugeno                       6.490469   \n",
       "MedianMethod      neighbourhood_size      1.0                          3.542016   \n",
       "                                          2.0                          3.195646   \n",
       "                                          1.5                          3.488536   \n",
       "                                          0.5                          7.533590   \n",
       "\n",
       "                                                              repetition  \n",
       "                                                                   count  \n",
       "algorithm         optim_param_name        optim_param_value               \n",
       "OceanWNN          wavelet_wbf             mexican_hat                168  \n",
       "                                          morlet                     168  \n",
       "                                          central_symmetric          168  \n",
       "                  wavelet_k               -1.9500000000000002        168  \n",
       "                                          -1.5                       168  \n",
       "                                          -1.0499999999999998        168  \n",
       "                  wavelet_cs_C            2.275                      168  \n",
       "                                          1.2249999999999999         168  \n",
       "                                          1.75                       168  \n",
       "                  wavelet_a               -3.25                      168  \n",
       "                                          -2.5                       168  \n",
       "                                          -1.75                      168  \n",
       "NumentaHTM        alpha                   0.1                        168  \n",
       "                                          0.5                        168  \n",
       "                                          0.9                        168  \n",
       "Normalizing Flows window_size             1.5                        193  \n",
       "                                          1.0                        193  \n",
       "                                          2.0                        193  \n",
       "                                          0.5                        193  \n",
       "                  teacher_epochs          100.0                      193  \n",
       "                                          20.0                       193  \n",
       "                                          50.0                       193  \n",
       "                                          10.0                       193  \n",
       "                  distillation_iterations 100.0                      193  \n",
       "                                          10.0                       193  \n",
       "                                          50.0                       193  \n",
       "                                          20.0                       193  \n",
       "MultiHMM          discretizer             choquet                    193  \n",
       "                                          fcm                        193  \n",
       "                                          sugeno                     193  \n",
       "MedianMethod      neighbourhood_size      1.0                        168  \n",
       "                                          2.0                        168  \n",
       "                                          1.5                        168  \n",
       "                                          0.5                        168  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_by = (\"ROC_AUC\", \"mean\")\n",
    "metric_agg_type = [\"min\", \"mean\", \"median\"]\n",
    "time_agg_type = \"mean\"\n",
    "aggs = {\n",
    "    \"PR_AUC\": metric_agg_type,\n",
    "    \"ROC_AUC\": metric_agg_type,\n",
    "    \"train_main_time\": time_agg_type,\n",
    "    \"execute_main_time\": time_agg_type,\n",
    "    \"repetition\": \"count\"\n",
    "}\n",
    "\n",
    "df_tmp = df.reset_index()\n",
    "df_tmp = df_tmp.groupby(by=[\"algorithm\", \"optim_param_name\", \"optim_param_value\"]).agg(aggs)\n",
    "df_tmp = df_tmp.reset_index()\n",
    "df_tmp = df_tmp.sort_values(by=[\"algorithm\", \"optim_param_name\", sort_by], ascending=False)\n",
    "df_tmp = df_tmp.set_index([\"algorithm\", \"optim_param_name\", \"optim_param_value\"])\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_tmp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selected parameters\n",
    "\n",
    "- OceanWNN: `wavelet_wbf=\"mexican_hat\",wavelet_cs_C=2.275,wavelet_k=-1.95,wavelet_a=-3.25` (no significant differences!)\n",
    "- NumentaHTM: `alpha=0.5` (no change at all! --> **also try to optimize fixed params**)\n",
    "- Normalizing Flows: `window_size=\"1.0 dataset period size\",teacher_epochs=100,distillation_iterations=100` (window_size: 1.5 fails on many datasets instead of better performance --> 1.0 is more robust)\n",
    "- MultiHMM: `discretizer=\"choquet\"`\n",
    "- MedianMethod: `neighbourhood_size=\"2.0 * dataset period size\"`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"MedianMethod\", \"neighbourhood_size\", 1),\n",
    "    (\"MedianMethod\", \"neighbourhood_size\", 2)\n",
    "], \"poly-combined-diff-2\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-9-964f728b5097>:48: UserWarning: No anomaly scores found! Probably Normalizing Flows was not executed on sinus-combined-diff-1 with params window_size=1.5.\n",
      "  warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name} with params {optim_params}.\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores([\n",
    "    (\"Normalizing Flows\", \"window_size\", 1.0),\n",
    "    (\"Normalizing Flows\", \"window_size\", 1.5)\n",
    "], \"sinus-combined-diff-1\", use_plotly=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval38",
   "language": "python",
   "name": "timeeval38"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
